Correlation-based feature ranking for online classification

Authors:
Hassab Elgawi Osman
Affiliations:
Imaging Science and Engineering Lab, Tokyo Institute of Technology, Japan
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

The contribution of this paper is two-fold. First, incremental feature selection based on correlation ranking (CR) is proposed for classification problems. Second, we develop online training mode using the random forests (RF) algorithm, then evaluate the performance of the combination based on the NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.